Overview

Dataset statistics

Number of variables21
Number of observations3351
Missing cells120
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory549.9 KiB
Average record size in memory168.0 B

Variable types

Categorical4
Text2
Numeric15

Alerts

month has constant value ""Constant
arr_flights is highly overall correlated with arr_del15 and 10 other fieldsHigh correlation
arr_del15 is highly overall correlated with arr_flights and 10 other fieldsHigh correlation
carrier_ct is highly overall correlated with arr_flights and 9 other fieldsHigh correlation
weather_ct is highly overall correlated with arr_flights and 9 other fieldsHigh correlation
nas_ct is highly overall correlated with arr_flights and 8 other fieldsHigh correlation
security_ct is highly overall correlated with security_delayHigh correlation
late_aircraft_ct is highly overall correlated with arr_flights and 9 other fieldsHigh correlation
arr_cancelled is highly overall correlated with arr_flights and 3 other fieldsHigh correlation
arr_delay is highly overall correlated with arr_flights and 10 other fieldsHigh correlation
carrier_delay is highly overall correlated with arr_flights and 10 other fieldsHigh correlation
weather_delay is highly overall correlated with arr_flights and 8 other fieldsHigh correlation
nas_delay is highly overall correlated with arr_flights and 9 other fieldsHigh correlation
security_delay is highly overall correlated with security_ctHigh correlation
late_aircraft_delay is highly overall correlated with arr_flights and 9 other fieldsHigh correlation
carrier is highly overall correlated with carrier_nameHigh correlation
carrier_name is highly overall correlated with carrierHigh correlation
arr_del15 has 161 (4.8%) zerosZeros
carrier_ct has 311 (9.3%) zerosZeros
weather_ct has 1623 (48.4%) zerosZeros
nas_ct has 527 (15.7%) zerosZeros
security_ct has 2982 (89.0%) zerosZeros
late_aircraft_ct has 588 (17.5%) zerosZeros
arr_cancelled has 1802 (53.8%) zerosZeros
arr_diverted has 2536 (75.7%) zerosZeros
arr_delay has 161 (4.8%) zerosZeros
carrier_delay has 311 (9.3%) zerosZeros
weather_delay has 1621 (48.4%) zerosZeros
nas_delay has 527 (15.7%) zerosZeros
security_delay has 2982 (89.0%) zerosZeros
late_aircraft_delay has 588 (17.5%) zerosZeros

Reproduction

Analysis started2023-10-24 00:09:11.278633
Analysis finished2023-10-24 00:10:27.428571
Duration1 minute and 16.15 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

year
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
2019
1812 
2020
1539 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters13404
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2019 1812
54.1%
2020 1539
45.9%

Length

2023-10-24T00:10:27.705539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T00:10:28.043320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2019 1812
54.1%
2020 1539
45.9%

Most occurring characters

ValueCountFrequency (%)
2 4890
36.5%
0 4890
36.5%
1 1812
 
13.5%
9 1812
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13404
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4890
36.5%
0 4890
36.5%
1 1812
 
13.5%
9 1812
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common 13404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4890
36.5%
0 4890
36.5%
1 1812
 
13.5%
9 1812
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4890
36.5%
0 4890
36.5%
1 1812
 
13.5%
9 1812
 
13.5%

month
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
12
3351 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6702
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 3351
100.0%

Length

2023-10-24T00:10:28.257794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T00:10:28.499927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
12 3351
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3351
50.0%
2 3351
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6702
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3351
50.0%
2 3351
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6702
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3351
50.0%
2 3351
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3351
50.0%
2 3351
50.0%

carrier
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
OO
482 
MQ
285 
DL
255 
G4
240 
9E
226 
Other values (12)
1863 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6702
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
OO 482
14.4%
MQ 285
 
8.5%
DL 255
 
7.6%
G4 240
 
7.2%
9E 226
 
6.7%
YV 219
 
6.5%
AA 199
 
5.9%
YX 196
 
5.8%
UA 193
 
5.8%
OH 187
 
5.6%
Other values (7) 869
25.9%

Length

2023-10-24T00:10:28.690873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oo 482
14.4%
mq 285
 
8.5%
dl 255
 
7.6%
g4 240
 
7.2%
9e 226
 
6.7%
yv 219
 
6.5%
aa 199
 
5.9%
yx 196
 
5.8%
ua 193
 
5.8%
oh 187
 
5.6%
Other values (7) 869
25.9%

Most occurring characters

ValueCountFrequency (%)
O 1151
17.2%
A 772
 
11.5%
Y 415
 
6.2%
9 410
 
6.1%
E 332
 
5.0%
V 325
 
4.8%
Q 285
 
4.3%
M 285
 
4.3%
N 278
 
4.1%
L 255
 
3.8%
Other values (12) 2194
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5932
88.5%
Decimal Number 770
 
11.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1151
19.4%
A 772
13.0%
Y 415
 
7.0%
E 332
 
5.6%
V 325
 
5.5%
Q 285
 
4.8%
M 285
 
4.8%
N 278
 
4.7%
L 255
 
4.3%
D 255
 
4.3%
Other values (9) 1579
26.6%
Decimal Number
ValueCountFrequency (%)
9 410
53.2%
4 240
31.2%
6 120
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5932
88.5%
Common 770
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1151
19.4%
A 772
13.0%
Y 415
 
7.0%
E 332
 
5.6%
V 325
 
5.5%
Q 285
 
4.8%
M 285
 
4.8%
N 278
 
4.7%
L 255
 
4.3%
D 255
 
4.3%
Other values (9) 1579
26.6%
Common
ValueCountFrequency (%)
9 410
53.2%
4 240
31.2%
6 120
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1151
17.2%
A 772
 
11.5%
Y 415
 
6.2%
9 410
 
6.1%
E 332
 
5.0%
V 325
 
4.8%
Q 285
 
4.3%
M 285
 
4.3%
N 278
 
4.1%
L 255
 
3.8%
Other values (12) 2194
32.7%

carrier_name
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
SkyWest Airlines Inc.
482 
Envoy Air
285 
Delta Air Lines Inc.
255 
Allegiant Air
240 
Endeavor Air Inc.
226 
Other values (12)
1863 

Length

Max length23
Median length21
Mean length18.188899
Min length9

Characters and Unicode

Total characters60951
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEndeavor Air Inc.
2nd rowEndeavor Air Inc.
3rd rowEndeavor Air Inc.
4th rowEndeavor Air Inc.
5th rowEndeavor Air Inc.

Common Values

ValueCountFrequency (%)
SkyWest Airlines Inc. 482
14.4%
Envoy Air 285
 
8.5%
Delta Air Lines Inc. 255
 
7.6%
Allegiant Air 240
 
7.2%
Endeavor Air Inc. 226
 
6.7%
Mesa Airlines Inc. 219
 
6.5%
American Airlines Inc. 199
 
5.9%
Republic Airline 196
 
5.8%
United Air Lines Inc. 193
 
5.8%
PSA Airlines Inc. 187
 
5.6%
Other values (7) 869
25.9%

Length

2023-10-24T00:10:28.967386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 2126
22.0%
airlines 1739
18.0%
air 1296
13.4%
lines 545
 
5.6%
skywest 482
 
5.0%
envoy 285
 
3.0%
delta 255
 
2.6%
allegiant 240
 
2.5%
endeavor 226
 
2.3%
mesa 219
 
2.3%
Other values (15) 2247
23.3%

Most occurring characters

ValueCountFrequency (%)
i 7111
11.7%
6309
 
10.4%
n 5970
 
9.8%
e 5307
 
8.7%
r 4347
 
7.1%
A 4121
 
6.8%
s 3642
 
6.0%
l 3130
 
5.1%
c 2521
 
4.1%
. 2307
 
3.8%
Other values (30) 16186
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41381
67.9%
Uppercase Letter 10954
 
18.0%
Space Separator 6309
 
10.4%
Other Punctuation 2307
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7111
17.2%
n 5970
14.4%
e 5307
12.8%
r 4347
10.5%
s 3642
8.8%
l 3130
7.6%
c 2521
 
6.1%
t 2039
 
4.9%
a 1658
 
4.0%
o 1057
 
2.6%
Other values (12) 4599
11.1%
Uppercase Letter
ValueCountFrequency (%)
A 4121
37.6%
I 2126
19.4%
S 947
 
8.6%
L 757
 
6.9%
E 617
 
5.6%
W 482
 
4.4%
C 287
 
2.6%
D 255
 
2.3%
J 226
 
2.1%
M 219
 
2.0%
Other values (6) 917
 
8.4%
Space Separator
ValueCountFrequency (%)
6309
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52335
85.9%
Common 8616
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7111
13.6%
n 5970
11.4%
e 5307
10.1%
r 4347
 
8.3%
A 4121
 
7.9%
s 3642
 
7.0%
l 3130
 
6.0%
c 2521
 
4.8%
I 2126
 
4.1%
t 2039
 
3.9%
Other values (28) 12021
23.0%
Common
ValueCountFrequency (%)
6309
73.2%
. 2307
 
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7111
11.7%
6309
 
10.4%
n 5970
 
9.8%
e 5307
 
8.7%
r 4347
 
7.1%
A 4121
 
6.8%
s 3642
 
6.0%
l 3130
 
5.1%
c 2521
 
4.1%
. 2307
 
3.8%
Other values (30) 16186
26.6%
Distinct360
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:29.620178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10053
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.6%

Sample

1st rowABE
2nd rowABY
3rd rowAEX
4th rowAGS
5th rowALB
ValueCountFrequency (%)
bna 31
 
0.9%
pit 31
 
0.9%
rdu 30
 
0.9%
msy 30
 
0.9%
dtw 29
 
0.9%
cle 29
 
0.9%
ind 29
 
0.9%
mci 28
 
0.8%
chs 28
 
0.8%
atl 28
 
0.8%
Other values (350) 3058
91.3%
2023-10-24T00:10:30.550256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 897
 
8.9%
A 872
 
8.7%
L 648
 
6.4%
M 600
 
6.0%
T 567
 
5.6%
C 565
 
5.6%
B 517
 
5.1%
R 517
 
5.1%
D 482
 
4.8%
P 462
 
4.6%
Other values (16) 3926
39.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10053
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 897
 
8.9%
A 872
 
8.7%
L 648
 
6.4%
M 600
 
6.0%
T 567
 
5.6%
C 565
 
5.6%
B 517
 
5.1%
R 517
 
5.1%
D 482
 
4.8%
P 462
 
4.6%
Other values (16) 3926
39.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 10053
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 897
 
8.9%
A 872
 
8.7%
L 648
 
6.4%
M 600
 
6.0%
T 567
 
5.6%
C 565
 
5.6%
B 517
 
5.1%
R 517
 
5.1%
D 482
 
4.8%
P 462
 
4.6%
Other values (16) 3926
39.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 897
 
8.9%
A 872
 
8.7%
L 648
 
6.4%
M 600
 
6.0%
T 567
 
5.6%
C 565
 
5.6%
B 517
 
5.1%
R 517
 
5.1%
D 482
 
4.8%
P 462
 
4.6%
Other values (16) 3926
39.1%
Distinct360
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:31.140559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length70
Median length57
Mean length41.829006
Min length20

Characters and Unicode

Total characters140169
Distinct characters59
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.6%

Sample

1st rowAllentown/Bethlehem/Easton, PA: Lehigh Valley International
2nd rowAlbany, GA: Southwest Georgia Regional
3rd rowAlexandria, LA: Alexandria International
4th rowAugusta, GA: Augusta Regional at Bush Field
5th rowAlbany, NY: Albany International
ValueCountFrequency (%)
international 2167
 
12.4%
regional 355
 
2.0%
fl 269
 
1.5%
tx 251
 
1.4%
ca 247
 
1.4%
city 227
 
1.3%
field 199
 
1.1%
san 186
 
1.1%
ny 165
 
0.9%
county 124
 
0.7%
Other values (760) 13240
76.0%
2023-10-24T00:10:32.080599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14079
 
10.0%
n 13035
 
9.3%
a 12623
 
9.0%
e 9215
 
6.6%
t 9200
 
6.6%
o 8777
 
6.3%
l 8032
 
5.7%
i 7820
 
5.6%
r 7071
 
5.0%
s 3804
 
2.7%
Other values (49) 46513
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96365
68.7%
Uppercase Letter 21854
 
15.6%
Space Separator 14079
 
10.0%
Other Punctuation 7524
 
5.4%
Dash Punctuation 347
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 13035
13.5%
a 12623
13.1%
e 9215
9.6%
t 9200
9.5%
o 8777
9.1%
l 8032
8.3%
i 7820
8.1%
r 7071
7.3%
s 3804
 
3.9%
u 2252
 
2.3%
Other values (16) 14536
15.1%
Uppercase Letter
ValueCountFrequency (%)
I 2875
 
13.2%
C 1772
 
8.1%
A 1758
 
8.0%
M 1380
 
6.3%
S 1271
 
5.8%
N 1187
 
5.4%
L 1153
 
5.3%
R 1073
 
4.9%
B 1051
 
4.8%
F 1024
 
4.7%
Other values (16) 7310
33.4%
Other Punctuation
ValueCountFrequency (%)
: 3351
44.5%
, 3351
44.5%
/ 676
 
9.0%
. 123
 
1.6%
' 23
 
0.3%
Space Separator
ValueCountFrequency (%)
14079
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 347
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118219
84.3%
Common 21950
 
15.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 13035
 
11.0%
a 12623
 
10.7%
e 9215
 
7.8%
t 9200
 
7.8%
o 8777
 
7.4%
l 8032
 
6.8%
i 7820
 
6.6%
r 7071
 
6.0%
s 3804
 
3.2%
I 2875
 
2.4%
Other values (42) 35767
30.3%
Common
ValueCountFrequency (%)
14079
64.1%
: 3351
 
15.3%
, 3351
 
15.3%
/ 676
 
3.1%
- 347
 
1.6%
. 123
 
0.6%
' 23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14079
 
10.0%
n 13035
 
9.3%
a 12623
 
9.0%
e 9215
 
6.6%
t 9200
 
6.6%
o 8777
 
6.3%
l 8032
 
5.7%
i 7820
 
5.6%
r 7071
 
5.0%
s 3804
 
2.7%
Other values (49) 46513
33.2%

arr_flights
Real number (ℝ)

HIGH CORRELATION 

Distinct751
Distinct (%)22.5%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean298.27101
Minimum1
Maximum19713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:32.415510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q135
median83
Q3194.5
95-th percentile1210.6
Maximum19713
Range19712
Interquartile range (IQR)159.5

Descriptive statistics

Standard deviation852.43633
Coefficient of variation (CV)2.8579255
Kurtosis123.74409
Mean298.27101
Median Absolute Deviation (MAD)57
Skewness8.6913344
Sum997120
Variance726647.71
MonotonicityNot monotonic
2023-10-24T00:10:32.720581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 98
 
2.9%
53 65
 
1.9%
62 46
 
1.4%
60 41
 
1.2%
2 39
 
1.2%
30 37
 
1.1%
12 37
 
1.1%
1 37
 
1.1%
14 34
 
1.0%
61 34
 
1.0%
Other values (741) 2875
85.8%
ValueCountFrequency (%)
1 37
1.1%
2 39
1.2%
3 14
 
0.4%
4 22
0.7%
5 8
 
0.2%
6 14
 
0.4%
7 12
 
0.4%
8 18
0.5%
9 30
0.9%
10 20
0.6%
ValueCountFrequency (%)
19713 1
< 0.1%
12549 1
< 0.1%
12420 1
< 0.1%
8423 1
< 0.1%
7965 1
< 0.1%
7959 1
< 0.1%
6411 1
< 0.1%
6345 1
< 0.1%
6217 1
< 0.1%
6103 1
< 0.1%

arr_del15
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct332
Distinct (%)9.9%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean50.995214
Minimum0
Maximum2289
Zeros161
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:33.050089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q333
95-th percentile212
Maximum2289
Range2289
Interquartile range (IQR)28

Descriptive statistics

Standard deviation146.48446
Coefficient of variation (CV)2.8725138
Kurtosis61.438774
Mean50.995214
Median Absolute Deviation (MAD)10
Skewness6.8610666
Sum170477
Variance21457.696
MonotonicityNot monotonic
2023-10-24T00:10:33.326113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 190
 
5.7%
2 162
 
4.8%
0 161
 
4.8%
4 152
 
4.5%
3 151
 
4.5%
5 130
 
3.9%
6 123
 
3.7%
9 118
 
3.5%
7 115
 
3.4%
10 111
 
3.3%
Other values (322) 1930
57.6%
ValueCountFrequency (%)
0 161
4.8%
1 190
5.7%
2 162
4.8%
3 151
4.5%
4 152
4.5%
5 130
3.9%
6 123
3.7%
7 115
3.4%
8 102
3.0%
9 118
3.5%
ValueCountFrequency (%)
2289 1
< 0.1%
2090 1
< 0.1%
1610 1
< 0.1%
1510 1
< 0.1%
1472 1
< 0.1%
1451 1
< 0.1%
1433 1
< 0.1%
1286 1
< 0.1%
1261 1
< 0.1%
1229 1
< 0.1%

carrier_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1601
Distinct (%)47.9%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean16.065337
Minimum0
Maximum697
Zeros311
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:33.604981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.49
median4.75
Q312.255
95-th percentile66.214
Maximum697
Range697
Interquartile range (IQR)10.765

Descriptive statistics

Standard deviation41.759516
Coefficient of variation (CV)2.5993552
Kurtosis73.625446
Mean16.065337
Median Absolute Deviation (MAD)3.75
Skewness7.1672095
Sum53706.42
Variance1743.8572
MonotonicityNot monotonic
2023-10-24T00:10:33.912394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311
 
9.3%
1 167
 
5.0%
2 67
 
2.0%
3 47
 
1.4%
4 28
 
0.8%
6 23
 
0.7%
5 22
 
0.7%
7 22
 
0.7%
9 14
 
0.4%
12 12
 
0.4%
Other values (1591) 2630
78.5%
ValueCountFrequency (%)
0 311
9.3%
0.01 2
 
0.1%
0.02 2
 
0.1%
0.03 3
 
0.1%
0.04 5
 
0.1%
0.05 2
 
0.1%
0.06 8
 
0.2%
0.08 5
 
0.1%
0.09 3
 
0.1%
0.1 2
 
0.1%
ValueCountFrequency (%)
697 1
< 0.1%
685.74 1
< 0.1%
550.92 1
< 0.1%
420.17 1
< 0.1%
414.32 1
< 0.1%
414.22 1
< 0.1%
391.23 1
< 0.1%
380.38 1
< 0.1%
364.08 1
< 0.1%
351.63 1
< 0.1%

weather_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct543
Distinct (%)16.2%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.4431439
Minimum0
Maximum89.42
Zeros1623
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:34.215301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.06
Q31.01
95-th percentile5.287
Maximum89.42
Range89.42
Interquartile range (IQR)1.01

Descriptive statistics

Standard deviation4.8216575
Coefficient of variation (CV)3.3410788
Kurtosis115.69527
Mean1.4431439
Median Absolute Deviation (MAD)0.06
Skewness9.3578077
Sum4824.43
Variance23.248381
MonotonicityNot monotonic
2023-10-24T00:10:34.495110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1623
48.4%
1 217
 
6.5%
2 53
 
1.6%
3 17
 
0.5%
0.48 13
 
0.4%
0.53 13
 
0.4%
0.07 13
 
0.4%
0.99 12
 
0.4%
0.05 12
 
0.4%
0.38 12
 
0.4%
Other values (533) 1358
40.5%
ValueCountFrequency (%)
0 1623
48.4%
0.01 10
 
0.3%
0.02 6
 
0.2%
0.03 10
 
0.3%
0.04 9
 
0.3%
0.05 12
 
0.4%
0.06 9
 
0.3%
0.07 13
 
0.4%
0.08 11
 
0.3%
0.09 11
 
0.3%
ValueCountFrequency (%)
89.42 1
< 0.1%
77 1
< 0.1%
71.76 1
< 0.1%
66.3 1
< 0.1%
64.15 1
< 0.1%
63 1
< 0.1%
59 1
< 0.1%
54 1
< 0.1%
46.6 1
< 0.1%
45.01 1
< 0.1%

nas_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1470
Distinct (%)44.0%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean16.183383
Minimum0
Maximum1039.54
Zeros527
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:34.791876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.82
median2.98
Q38.87
95-th percentile69.095
Maximum1039.54
Range1039.54
Interquartile range (IQR)8.05

Descriptive statistics

Standard deviation56.423008
Coefficient of variation (CV)3.4864779
Kurtosis104.17147
Mean16.183383
Median Absolute Deviation (MAD)2.98
Skewness8.7790581
Sum54101.05
Variance3183.5559
MonotonicityNot monotonic
2023-10-24T00:10:35.104025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 527
 
15.7%
1 150
 
4.5%
2 50
 
1.5%
3 32
 
1.0%
0.07 9
 
0.3%
1.27 9
 
0.3%
1.48 9
 
0.3%
0.02 9
 
0.3%
1.03 8
 
0.2%
0.4 8
 
0.2%
Other values (1460) 2532
75.6%
ValueCountFrequency (%)
0 527
15.7%
0.01 2
 
0.1%
0.02 9
 
0.3%
0.03 5
 
0.1%
0.04 2
 
0.1%
0.05 7
 
0.2%
0.06 8
 
0.2%
0.07 9
 
0.3%
0.08 3
 
0.1%
0.09 4
 
0.1%
ValueCountFrequency (%)
1039.54 1
< 0.1%
915.72 1
< 0.1%
761.79 1
< 0.1%
758.43 1
< 0.1%
707.34 1
< 0.1%
652.73 1
< 0.1%
650.12 1
< 0.1%
637.24 1
< 0.1%
626.41 1
< 0.1%
497.19 1
< 0.1%

security_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct164
Distinct (%)4.9%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.13731977
Minimum0
Maximum17.31
Zeros2982
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:35.399433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17.31
Range17.31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64647884
Coefficient of variation (CV)4.707835
Kurtosis191.29528
Mean0.13731977
Median Absolute Deviation (MAD)0
Skewness10.733895
Sum459.06
Variance0.41793488
MonotonicityNot monotonic
2023-10-24T00:10:35.694458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2982
89.0%
1 79
 
2.4%
0.05 7
 
0.2%
0.03 6
 
0.2%
2 5
 
0.1%
0.96 5
 
0.1%
0.13 4
 
0.1%
0.94 4
 
0.1%
0.45 4
 
0.1%
1.15 4
 
0.1%
Other values (154) 243
 
7.3%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 2982
89.0%
0.02 1
 
< 0.1%
0.03 6
 
0.2%
0.04 3
 
0.1%
0.05 7
 
0.2%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 3
 
0.1%
0.1 2
 
0.1%
0.11 2
 
0.1%
ValueCountFrequency (%)
17.31 1
< 0.1%
9.02 1
< 0.1%
7.56 1
< 0.1%
7 1
< 0.1%
6.28 1
< 0.1%
6.18 1
< 0.1%
6.14 1
< 0.1%
5.83 1
< 0.1%
5.78 1
< 0.1%
5.76 1
< 0.1%

late_aircraft_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1506
Distinct (%)45.0%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean17.166069
Minimum0
Maximum819.66
Zeros588
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:36.013214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.9
median3.28
Q310.24
95-th percentile67.898
Maximum819.66
Range819.66
Interquartile range (IQR)9.34

Descriptive statistics

Standard deviation55.447043
Coefficient of variation (CV)3.2300372
Kurtosis68.782659
Mean17.166069
Median Absolute Deviation (MAD)3.28
Skewness7.3744247
Sum57386.17
Variance3074.3746
MonotonicityNot monotonic
2023-10-24T00:10:36.296154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 588
 
17.5%
1 145
 
4.3%
2 58
 
1.7%
3 23
 
0.7%
4 18
 
0.5%
5 15
 
0.4%
1.96 9
 
0.3%
1.88 9
 
0.3%
0.87 9
 
0.3%
9 7
 
0.2%
Other values (1496) 2462
73.5%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 588
17.5%
0.01 2
 
0.1%
0.03 4
 
0.1%
0.04 6
 
0.2%
0.05 2
 
0.1%
0.06 3
 
0.1%
0.07 1
 
< 0.1%
0.08 3
 
0.1%
0.09 1
 
< 0.1%
0.11 2
 
0.1%
ValueCountFrequency (%)
819.66 1
< 0.1%
781.29 1
< 0.1%
753.59 1
< 0.1%
635.79 1
< 0.1%
582.52 1
< 0.1%
545.79 1
< 0.1%
544.87 1
< 0.1%
541.76 1
< 0.1%
541.04 1
< 0.1%
519.51 1
< 0.1%

arr_cancelled
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)2.2%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.8845348
Minimum0
Maximum224
Zeros1802
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:36.596656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile12
Maximum224
Range224
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.126658
Coefficient of variation (CV)3.5106728
Kurtosis125.29169
Mean2.8845348
Median Absolute Deviation (MAD)0
Skewness9.2297791
Sum9643
Variance102.5492
MonotonicityNot monotonic
2023-10-24T00:10:36.885849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1802
53.8%
1 585
 
17.5%
2 271
 
8.1%
3 166
 
5.0%
4 104
 
3.1%
5 67
 
2.0%
6 48
 
1.4%
9 33
 
1.0%
8 32
 
1.0%
7 31
 
0.9%
Other values (62) 204
 
6.1%
ValueCountFrequency (%)
0 1802
53.8%
1 585
 
17.5%
2 271
 
8.1%
3 166
 
5.0%
4 104
 
3.1%
5 67
 
2.0%
6 48
 
1.4%
7 31
 
0.9%
8 32
 
1.0%
9 33
 
1.0%
ValueCountFrequency (%)
224 1
< 0.1%
157 1
< 0.1%
138 1
< 0.1%
129 1
< 0.1%
116 1
< 0.1%
110 1
< 0.1%
101 1
< 0.1%
86 1
< 0.1%
84 1
< 0.1%
83 1
< 0.1%

arr_diverted
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)0.7%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.57583009
Minimum0
Maximum42
Zeros2536
Zeros (%)75.7%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:37.168071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0978835
Coefficient of variation (CV)3.6432335
Kurtosis154.61376
Mean0.57583009
Median Absolute Deviation (MAD)0
Skewness10.503617
Sum1925
Variance4.4011152
MonotonicityNot monotonic
2023-10-24T00:10:37.402700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 2536
75.7%
1 491
 
14.7%
2 133
 
4.0%
3 67
 
2.0%
4 35
 
1.0%
5 22
 
0.7%
6 14
 
0.4%
10 7
 
0.2%
8 7
 
0.2%
11 7
 
0.2%
Other values (13) 24
 
0.7%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 2536
75.7%
1 491
 
14.7%
2 133
 
4.0%
3 67
 
2.0%
4 35
 
1.0%
5 22
 
0.7%
6 14
 
0.4%
7 5
 
0.1%
8 7
 
0.2%
9 4
 
0.1%
ValueCountFrequency (%)
42 1
< 0.1%
38 2
0.1%
32 1
< 0.1%
31 1
< 0.1%
25 1
< 0.1%
24 1
< 0.1%
21 2
0.1%
18 1
< 0.1%
17 1
< 0.1%
14 2
0.1%

arr_delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2032
Distinct (%)60.8%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3333.8681
Minimum0
Maximum160383
Zeros161
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:37.673160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.1
Q1230
median746
Q32095.5
95-th percentile12878.1
Maximum160383
Range160383
Interquartile range (IQR)1865.5

Descriptive statistics

Standard deviation10284.927
Coefficient of variation (CV)3.0849831
Kurtosis72.22555
Mean3333.8681
Median Absolute Deviation (MAD)631
Skewness7.4522941
Sum11145121
Variance1.0577972 × 108
MonotonicityNot monotonic
2023-10-24T00:10:38.145797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 161
 
4.8%
23 10
 
0.3%
51 8
 
0.2%
19 8
 
0.2%
92 8
 
0.2%
18 8
 
0.2%
178 8
 
0.2%
20 7
 
0.2%
114 7
 
0.2%
17 7
 
0.2%
Other values (2022) 3111
92.8%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 161
4.8%
15 7
 
0.2%
16 7
 
0.2%
17 7
 
0.2%
18 8
 
0.2%
19 8
 
0.2%
20 7
 
0.2%
21 2
 
0.1%
22 5
 
0.1%
23 10
 
0.3%
ValueCountFrequency (%)
160383 1
< 0.1%
146063 1
< 0.1%
131775 1
< 0.1%
129297 1
< 0.1%
120448 1
< 0.1%
117498 1
< 0.1%
109305 1
< 0.1%
96011 1
< 0.1%
89508 1
< 0.1%
88488 1
< 0.1%

carrier_delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1431
Distinct (%)42.8%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1144.7631
Minimum0
Maximum55215
Zeros311
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:38.678314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q168.5
median272
Q3830.5
95-th percentile4492.8
Maximum55215
Range55215
Interquartile range (IQR)762

Descriptive statistics

Standard deviation3371.1035
Coefficient of variation (CV)2.9448045
Kurtosis87.983583
Mean1144.7631
Median Absolute Deviation (MAD)249
Skewness7.9937905
Sum3826943
Variance11364339
MonotonicityNot monotonic
2023-10-24T00:10:39.181094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311
 
9.3%
16 15
 
0.4%
21 14
 
0.4%
34 13
 
0.4%
87 12
 
0.4%
5 12
 
0.4%
20 12
 
0.4%
64 12
 
0.4%
85 12
 
0.4%
10 12
 
0.4%
Other values (1421) 2918
87.1%
ValueCountFrequency (%)
0 311
9.3%
1 8
 
0.2%
2 12
 
0.4%
3 3
 
0.1%
4 6
 
0.2%
5 12
 
0.4%
6 5
 
0.1%
7 2
 
0.1%
8 5
 
0.1%
9 6
 
0.2%
ValueCountFrequency (%)
55215 1
< 0.1%
55006 1
< 0.1%
50933 1
< 0.1%
44343 1
< 0.1%
38827 1
< 0.1%
37593 1
< 0.1%
35539 1
< 0.1%
32486 1
< 0.1%
29667 1
< 0.1%
28264 1
< 0.1%

weather_delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct589
Distinct (%)17.6%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean177.59138
Minimum0
Maximum14219
Zeros1621
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:39.705471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q382
95-th percentile768.9
Maximum14219
Range14219
Interquartile range (IQR)82

Descriptive statistics

Standard deviation734.34354
Coefficient of variation (CV)4.1350178
Kurtosis136.12166
Mean177.59138
Median Absolute Deviation (MAD)3
Skewness10.293105
Sum593688
Variance539260.44
MonotonicityNot monotonic
2023-10-24T00:10:40.223210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1621
48.4%
2 28
 
0.8%
15 26
 
0.8%
13 25
 
0.7%
8 24
 
0.7%
22 23
 
0.7%
10 23
 
0.7%
20 22
 
0.7%
16 22
 
0.7%
29 20
 
0.6%
Other values (579) 1509
45.0%
ValueCountFrequency (%)
0 1621
48.4%
1 13
 
0.4%
2 28
 
0.8%
3 17
 
0.5%
4 14
 
0.4%
5 13
 
0.4%
6 15
 
0.4%
7 18
 
0.5%
8 24
 
0.7%
9 20
 
0.6%
ValueCountFrequency (%)
14219 1
< 0.1%
12655 1
< 0.1%
10894 1
< 0.1%
10048 1
< 0.1%
9430 1
< 0.1%
9357 1
< 0.1%
8980 1
< 0.1%
8651 1
< 0.1%
8441 1
< 0.1%
8267 1
< 0.1%

nas_delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1014
Distinct (%)30.3%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean749.57942
Minimum0
Maximum82064
Zeros527
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:40.783849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.5
median106
Q3362
95-th percentile2968.4
Maximum82064
Range82064
Interquartile range (IQR)340.5

Descriptive statistics

Standard deviation3190.5092
Coefficient of variation (CV)4.2563991
Kurtosis223.08641
Mean749.57942
Median Absolute Deviation (MAD)106
Skewness12.49362
Sum2505844
Variance10179349
MonotonicityNot monotonic
2023-10-24T00:10:41.303143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 527
 
15.7%
17 26
 
0.8%
18 22
 
0.7%
25 21
 
0.6%
16 21
 
0.6%
51 20
 
0.6%
56 18
 
0.5%
21 18
 
0.5%
15 18
 
0.5%
32 18
 
0.5%
Other values (1004) 2634
78.6%
ValueCountFrequency (%)
0 527
15.7%
1 14
 
0.4%
2 12
 
0.4%
3 14
 
0.4%
4 9
 
0.3%
5 13
 
0.4%
6 16
 
0.5%
7 8
 
0.2%
8 11
 
0.3%
9 15
 
0.4%
ValueCountFrequency (%)
82064 1
< 0.1%
61151 1
< 0.1%
52812 1
< 0.1%
45481 1
< 0.1%
42011 1
< 0.1%
38907 1
< 0.1%
26876 1
< 0.1%
26323 1
< 0.1%
26097 1
< 0.1%
25024 1
< 0.1%

security_delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct125
Distinct (%)3.7%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.4008376
Minimum0
Maximum553
Zeros2982
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:41.802514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile31
Maximum553
Range553
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.161402
Coefficient of variation (CV)5.0291091
Kurtosis137.61281
Mean5.4008376
Median Absolute Deviation (MAD)0
Skewness10.007764
Sum18055
Variance737.74173
MonotonicityNot monotonic
2023-10-24T00:10:42.328700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2982
89.0%
16 15
 
0.4%
4 13
 
0.4%
17 12
 
0.4%
11 11
 
0.3%
7 11
 
0.3%
18 10
 
0.3%
15 8
 
0.2%
36 8
 
0.2%
19 7
 
0.2%
Other values (115) 266
 
7.9%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 2982
89.0%
1 5
 
0.1%
2 6
 
0.2%
3 5
 
0.1%
4 13
 
0.4%
5 4
 
0.1%
6 2
 
0.1%
7 11
 
0.3%
8 6
 
0.2%
9 4
 
0.1%
ValueCountFrequency (%)
553 1
< 0.1%
496 1
< 0.1%
391 1
< 0.1%
376 1
< 0.1%
364 1
< 0.1%
345 1
< 0.1%
297 1
< 0.1%
274 1
< 0.1%
254 1
< 0.1%
226 1
< 0.1%

late_aircraft_delay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1354
Distinct (%)40.5%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1256.5334
Minimum0
Maximum75179
Zeros588
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size26.3 KiB
2023-10-24T00:10:42.841872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median205
Q3724
95-th percentile4820.8
Maximum75179
Range75179
Interquartile range (IQR)693

Descriptive statistics

Standard deviation4184.4514
Coefficient of variation (CV)3.3301555
Kurtosis81.848748
Mean1256.5334
Median Absolute Deviation (MAD)205
Skewness7.8422796
Sum4200591
Variance17509634
MonotonicityNot monotonic
2023-10-24T00:10:43.125215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 588
 
17.5%
7 14
 
0.4%
92 14
 
0.4%
32 14
 
0.4%
16 14
 
0.4%
19 13
 
0.4%
83 13
 
0.4%
30 12
 
0.4%
36 12
 
0.4%
15 12
 
0.4%
Other values (1344) 2637
78.7%
ValueCountFrequency (%)
0 588
17.5%
1 6
 
0.2%
2 7
 
0.2%
3 9
 
0.3%
4 6
 
0.2%
5 5
 
0.1%
6 6
 
0.2%
7 14
 
0.4%
8 10
 
0.3%
9 5
 
0.1%
ValueCountFrequency (%)
75179 1
< 0.1%
57253 1
< 0.1%
56225 1
< 0.1%
50291 1
< 0.1%
43519 1
< 0.1%
42588 1
< 0.1%
41463 1
< 0.1%
41251 1
< 0.1%
37973 1
< 0.1%
37066 1
< 0.1%

Interactions

2023-10-24T00:10:17.808546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:14.811660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:18.885886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:23.881871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:28.485372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:32.223296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:36.476286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:42.233072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:46.571515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:50.331380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:55.509654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:59.954854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:04.097998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:08.728281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:13.672464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:18.238871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:15.124237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:19.143697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:24.329751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:28.754920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:32.753566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:36.905550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:42.670777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:46.859777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:50.586053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:55.953650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:00.210254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:04.369659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:09.150624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:13.945703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:18.631724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:15.373045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:19.380318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:24.719461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:28.992047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:32.991006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:37.310383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:42.921426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:47.088735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:50.823507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:56.370124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:00.479262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:04.620144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:09.535039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:14.185184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:19.025838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:15.661554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:19.641175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:25.156021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:29.254401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:33.251049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:37.720743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:43.177706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:47.340227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:51.089848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:56.775593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:00.730517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:04.894772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:09.976005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:14.444162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:19.435599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:15.911216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:19.887539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:25.510947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:29.487526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:33.497064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:38.120296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:43.537678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:47.582693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:51.428008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:57.033051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:01.354428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:05.147551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:10.272143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:14.684278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:19.842757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:16.185479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:20.120410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:25.871892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:29.732576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:33.742094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:38.516114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:43.796215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:47.820972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:51.779121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:57.305110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:01.599247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:05.409527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:10.634580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:14.924895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:20.245488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:16.453591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:20.432473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:26.143510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:29.993966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:33.999845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:38.910398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:44.058323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:48.094679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:52.175842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:57.579523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:01.862594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:05.692847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:11.077957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:15.208727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:20.644307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:16.736741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:20.774071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:26.402405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:30.225970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:34.234393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:39.304972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:44.293314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:48.340265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:52.563546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:57.830724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:02.093868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:05.946322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:11.470732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:15.452392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:21.035284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:16.988015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:21.154304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:26.647129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:30.459098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:34.485479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:39.697344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:44.522968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:48.559807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:52.902211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:58.079548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:02.328049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:06.188773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:11.866867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:15.701449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:22.024744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:17.239949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:21.537058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:26.886436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:30.702509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:34.726071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:40.079725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:44.769307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:48.794561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:53.283052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:58.339828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:02.570448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:06.436776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:12.162785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:15.935261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:22.541539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:17.539776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:21.958647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:27.174450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:30.964885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:34.992855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:40.501987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:45.029950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:49.062503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:53.722356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:58.613540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:02.834070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:06.736528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:12.433800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:16.227149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:22.897867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:17.816354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:22.308244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:27.427040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:31.205953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:35.234286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:40.930977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:45.264374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:49.306626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:54.132686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:58.874401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:03.074884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:07.084717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:12.677852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:16.470539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:23.281728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:18.094393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:22.891488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:27.688892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:31.483686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:35.503019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:41.280960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:45.828429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:49.573730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:54.466424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:59.147317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:03.334327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:07.476023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:12.934512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:16.742576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:23.608287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:18.349605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:23.198888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:27.942143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:31.712236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:35.738313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:41.530976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:46.064244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:49.822514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:54.782121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:59.407482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:03.589918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:07.881523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:13.151853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:17.005903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:23.996888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:18.602913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:23.598077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:28.215531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:31.960234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:36.062870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:41.968760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:46.315477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:50.080770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:55.133273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:09:59.675195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:03.843050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:08.288623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:13.399159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-24T00:10:17.394288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-24T00:10:43.974202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
arr_flightsarr_del15carrier_ctweather_ctnas_ctsecurity_ctlate_aircraft_ctarr_cancelledarr_divertedarr_delaycarrier_delayweather_delaynas_delaysecurity_delaylate_aircraft_delayyearcarriercarrier_name
arr_flights1.0000.9200.8910.5960.7830.3790.7970.5420.4210.8630.8360.5770.7600.3790.7600.0150.0780.078
arr_del150.9201.0000.9370.6270.8490.3890.8980.5440.4130.9580.9000.6110.8370.3890.8680.1010.0770.077
carrier_ct0.8910.9371.0000.5580.7060.3640.7800.4980.3980.8940.9500.5390.6940.3640.7450.0750.0950.095
weather_ct0.5960.6270.5581.0000.5040.2610.5490.4190.3670.6420.5480.9780.5200.2600.5370.0210.0000.000
nas_ct0.7830.8490.7060.5041.0000.3640.7370.4790.3480.7960.6870.4970.9720.3640.7270.0760.0430.043
security_ct0.3790.3890.3640.2610.3641.0000.3570.2870.2350.3760.3540.2480.3560.9990.3520.0000.0900.090
late_aircraft_ct0.7970.8980.7800.5490.7370.3571.0000.4930.3800.8860.7660.5370.7370.3570.9720.1370.0880.088
arr_cancelled0.5420.5440.4980.4190.4790.2870.4931.0000.3470.5570.5050.4190.4910.2850.4980.0000.0410.041
arr_diverted0.4210.4130.3980.3670.3480.2350.3800.3471.0000.4150.3950.3550.3540.2340.3760.0590.0260.026
arr_delay0.8630.9580.8940.6420.7960.3760.8860.5570.4151.0000.9220.6440.8150.3750.8920.1050.0480.048
carrier_delay0.8360.9000.9500.5480.6870.3540.7660.5050.3950.9221.0000.5330.6860.3530.7500.0740.0570.057
weather_delay0.5770.6110.5390.9780.4970.2480.5370.4190.3550.6440.5331.0000.5160.2470.5320.0350.0000.000
nas_delay0.7600.8370.6940.5200.9720.3560.7370.4910.3540.8150.6860.5161.0000.3560.7320.0810.0330.033
security_delay0.3790.3890.3640.2600.3640.9990.3570.2850.2340.3750.3530.2470.3561.0000.3520.0330.0570.057
late_aircraft_delay0.7600.8680.7450.5370.7270.3520.9720.4980.3760.8920.7500.5320.7320.3521.0000.1290.0590.059
year0.0150.1010.0750.0210.0760.0000.1370.0000.0590.1050.0740.0350.0810.0330.1291.0000.1610.161
carrier0.0780.0770.0950.0000.0430.0900.0880.0410.0260.0480.0570.0000.0330.0570.0590.1611.0001.000
carrier_name0.0780.0770.0950.0000.0430.0900.0880.0410.0260.0480.0570.0000.0330.0570.0590.1611.0001.000

Missing values

2023-10-24T00:10:24.674061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-24T00:10:25.852281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-24T00:10:26.841992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearmonthcarriercarrier_nameairportairport_namearr_flightsarr_del15carrier_ctweather_ctnas_ctsecurity_ctlate_aircraft_ctarr_cancelledarr_divertedarr_delaycarrier_delayweather_delaynas_delaysecurity_delaylate_aircraft_delay
02020129EEndeavor Air Inc.ABEAllentown/Bethlehem/Easton, PA: Lehigh Valley International44.03.01.630.000.120.01.250.01.089.056.00.03.00.030.0
12020129EEndeavor Air Inc.ABYAlbany, GA: Southwest Georgia Regional90.01.00.960.000.040.00.000.00.023.022.00.01.00.00.0
22020129EEndeavor Air Inc.AEXAlexandria, LA: Alexandria International88.08.05.750.001.600.00.650.01.0338.0265.00.045.00.028.0
32020129EEndeavor Air Inc.AGSAugusta, GA: Augusta Regional at Bush Field184.09.04.170.001.830.03.000.00.0508.0192.00.092.00.0224.0
42020129EEndeavor Air Inc.ALBAlbany, NY: Albany International76.011.04.780.005.220.01.001.00.0692.0398.00.0178.00.0116.0
52020129EEndeavor Air Inc.ATLAtlanta, GA: Hartsfield-Jackson Atlanta International5985.0445.0142.8911.96161.371.0127.795.00.030756.016390.01509.05060.016.07781.0
62020129EEndeavor Air Inc.ATWAppleton, WI: Appleton International142.014.05.360.007.700.00.941.00.0436.0162.00.0182.00.092.0
72020129EEndeavor Air Inc.AVLAsheville, NC: Asheville Regional147.010.06.041.001.000.01.960.01.01070.0838.0141.024.00.067.0
82020129EEndeavor Air Inc.AZOKalamazoo, MI: Kalamazoo/Battle Creek International84.014.06.240.966.800.00.001.01.02006.01164.0619.0223.00.00.0
92020129EEndeavor Air Inc.BDLHartford, CT: Bradley International150.019.05.700.0012.070.01.233.00.0846.0423.00.0389.00.034.0
yearmonthcarriercarrier_nameairportairport_namearr_flightsarr_del15carrier_ctweather_ctnas_ctsecurity_ctlate_aircraft_ctarr_cancelledarr_divertedarr_delaycarrier_delayweather_delaynas_delaysecurity_delaylate_aircraft_delay
3341201912DLDelta Air Lines Inc.VPSValparaiso, FL: Eglin AFB Destin Fort Walton Beach152.011.07.060.481.130.02.330.00.0620.0498.013.034.00.075.0
3342201912DLDelta Air Lines Inc.XNAFayetteville, AR: Northwest Arkansas Regional36.01.01.000.000.000.00.000.01.017.017.00.00.00.00.0
3343201912EVExpressJet Airlines LLCABQAlbuquerque, NM: Albuquerque International Sunport44.012.06.040.923.630.01.410.00.01397.01028.0102.0163.00.0104.0
3344201912EVExpressJet Airlines LLCAEXAlexandria, LA: Alexandria International75.09.02.381.000.010.05.611.00.0605.082.028.01.00.0494.0
3345201912EVExpressJet Airlines LLCALBAlbany, NY: Albany International16.012.04.230.003.030.04.751.00.0858.0443.00.0186.00.0229.0
3346201912EVExpressJet Airlines LLCAMAAmarillo, TX: Rick Husband Amarillo International56.08.02.201.003.990.00.800.01.0353.0165.019.0135.00.034.0
3347201912EVExpressJet Airlines LLCATLAtlanta, GA: Hartsfield-Jackson Atlanta International76.017.07.510.244.130.05.120.00.01880.01516.025.0200.00.0139.0
3348201912EVExpressJet Airlines LLCAUSAustin, TX: Austin - Bergstrom International7.01.00.000.480.520.00.000.00.096.00.046.050.00.00.0
3349201912EVExpressJet Airlines LLCAVLAsheville, NC: Asheville Regional12.01.00.000.001.000.00.000.00.023.00.00.023.00.00.0
3350201912EVExpressJet Airlines LLCAZOKalamazoo, MI: Kalamazoo/Battle Creek International9.01.00.870.000.130.00.000.00.023.020.00.03.00.00.0